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Record W4366982370 · doi:10.1177/19312431231165931

Failure or Reasonable Move?: Portrayal of American Pullout from Afghanistan in Russian and American International Media

2023· article· en· W4366982370 on OpenAlexaff
Ivanka Pjesivac, Iveta Imre, Leslie Klein, Ana Petrov

Bibliographic record

VenueElectronic News · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNarrativeFaithPolitical scienceAsian americansNews mediaSociologyGender studiesSocial psychologyLawMedia studiesCriminologyPsychology

Abstract

fetched live from OpenAlex

This study examined competing narratives about the actions of the United States abroad through an analysis of 314 news items of the 2021 American withdrawal from Afghanistan by two media organizations: Russian Sputnik and American Radio Free Europe/Radio Liberty. The textual analysis revealed five dominant themes in Sputnik: Americans as losers and Taliban as winners, Americans as incompetent, Americans as unreliable, insensitive, and irresponsible, Americans as duplicitous, and Americans as a threat to world peace; and four dominant themes in RFE/RL: The Taliban taking over the country quickly, Americans successfully leading the evacuations, Americans having good faith and defending the decision to pull out, and Americans as naïve and lacking good judgment. The findings are interpreted in light of the image theory and second-level agenda setting theory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.004
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.305
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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